Abstract

Web Ontology Language ontologies become more and more popular in complex domain modeling for their high expressiveness, flexibility and well defined semantics. Although query languages adequate in expressiveness to OWL reasoning capabilities were introduced before, their implementations are rather limited. In this paper, the authors study SPARQL-DLNOT, an extension of one of these query languages, SPARQL-DL, and present novel evaluation and optimization techniques for efficient SPARQL-DLNOT execution. As queries become complex easily, they also present a novel graph-based visualization that simplifies query construction and maintenance. Presented techniques and algorithms were implemented in the Pellet reasoner and in their novel Protégé plug-in OWL2Query.

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Related Work

During last years several conjunctive query and meta-query languages were introduced and implemented on top of existing OWL reasoners. Most of the efforts have been spent on conjunctive ABox queries (i.e. queries that retrieve only ABox individuals). Authors of (Horrocks & Tessaris, 2000) propose methods for conjunctive ABox query answering in the ALC language (Baader, Calvanese, McGuinness, Nardi, & Patel-Schneider, 2003) that transforms a conjunctive query answering problem to instance checking or instance retrieval problem. Although for ALC the proposed technique works fine, the authors noticed that its generalization is problematic for expressive description logics backing OWL or OWL 2 and is possible only for queries that do not contain cyclic structures of undistinguished variables (see section Preliminaries for more details). Evaluation and optimization of conjunctive ABox queries in expressive description logics are discussed also in Sirin and Parsia (2006), Ortiz, Calvanese, and Eiter (2006) and in Dolby et al. (2008).

Conjunctive ABox queries allow retrieving individuals and literals from an OWL ontology, thus being similar to SQL query language for relational databases. However, OWL ontologies contain also significant amount of knowledge in TBox and RBox to model taxonomies and complex characteristics of classes (disjointness, equivalence, etc.) or properties (transitivity, functionality). To address this issue, query languages SPARQL-DL, SQWRL (O’Connor & Das, 2009) and OWL-SAIQL (Kubias, Schenk, Staab, & Pan, 2007) appeared during last years that allow evaluating mixed ABox, TBox and RBox queries to retrieve individuals, classes, and properties.